from escnn.gspaces import *
from escnn.nn import FieldType
from escnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch.nn.functional as F
import torch
from typing import List, Tuple, Any, Union
import math
__all__ = [
"PointwiseMaxPool2D", "PointwiseMaxPool",
"PointwiseMaxPool3D",
"PointwiseMaxPoolAntialiased2D", "PointwiseMaxPoolAntialiased",
"PointwiseMaxPoolAntialiased3D",
]
class _PointwiseMaxPoolND(EquivariantModule):
def __init__(self,
in_type: FieldType,
d: int,
kernel_size: Union[int, Tuple],
stride: Union[int, Tuple] = None,
padding: Union[int, Tuple] = 0,
dilation: Union[int, Tuple] = 1,
ceil_mode: bool = False
):
r"""
Channel-wise max-pooling: each channel is treated independently.
This module works exactly as :class:`torch.nn.MaxPool2D` or :class:`torch.nn.MaxPool3D`, wrapping it in the
:class:`~escnn.nn.EquivariantModule` interface.
Notice that not all representations support this kind of pooling. In general, only representations which support
pointwise non-linearities do.
.. warning ::
Even if the input tensor has a `coords` attribute, the output of this module will not have one.
Args:
in_type (FieldType): the input field type
d (int): dimensionality of the base space (2 for images, 3 for volumes)
kernel_size: the size of the window to take a max over
stride: the stride of the window. Default value is :attr:`kernel_size`
padding: implicit zero padding to be added on both sides
dilation: a parameter that controls the stride of elements in the window
ceil_mode: when True, will use ceil instead of floor to compute the output shape
"""
assert d in [2, 3], f"Only dimensionality 2 or 3 are currently suported by 'd={d}' found"
assert isinstance(in_type.gspace, GSpace)
assert in_type.gspace.dimensionality == d, (in_type.gspace.dimensionality, d)
for r in in_type.representations:
assert 'pointwise' in r.supported_nonlinearities, \
f"""Error! Representation "{r.name}" does not support pointwise non-linearities
so it is not possible to pool each channel independently"""
super(_PointwiseMaxPoolND, self).__init__()
self.d = d
self.space = in_type.gspace
self.in_type = in_type
self.out_type = in_type
if isinstance(kernel_size, int):
self.kernel_size = (kernel_size,) * self.d
else:
self.kernel_size = kernel_size
if isinstance(stride, int):
self.stride = (stride,) * self.d
elif stride is None:
self.stride = self.kernel_size
else:
self.stride = stride
if isinstance(padding, int):
self.padding = (padding,) * self.d
else:
self.padding = padding
if isinstance(dilation, int):
self.dilation = (dilation,) * self.d
else:
self.dilation = dilation
self.ceil_mode = ceil_mode
def forward(self, input: GeometricTensor) -> GeometricTensor:
r"""
Args:
input (GeometricTensor): the input feature map
Returns:
the resulting feature map
"""
assert input.type == self.in_type
# run the common max-pooling
if self.d == 2:
output = F.max_pool2d(input.tensor,
self.kernel_size,
self.stride,
self.padding,
self.dilation,
self.ceil_mode)
elif self.d == 3:
output = F.max_pool3d(input.tensor,
self.kernel_size,
self.stride,
self.padding,
self.dilation,
self.ceil_mode)
else:
raise NotImplementedError
# wrap the result in a GeometricTensor
return GeometricTensor(output, self.out_type, coords=None)
def evaluate_output_shape(self, input_shape: Tuple) -> Tuple:
assert len(input_shape) == 2 + self.d, (input_shape, self.d)
assert input_shape[1] == self.in_type.size
b, c = input_shape[:2]
shape_i = input_shape[2:]
# compute the output shape (see 'torch.nn.MaxPool2D')
shape_o = tuple(
(shape_i[i] + 2 * self.padding[i] - self.dilation[i] * (self.kernel_size[i] - 1) - 1) / self.stride[i] + 1
for i in range(self.d)
)
if self.ceil_mode:
shape_o = tuple(
math.ceil(shape_o[i])
for i in range(self.d)
)
else:
shape_o = tuple(
math.floor(shape_o[i])
for i in range(self.d)
)
return (b, self.out_type.size, *shape_o)
def check_equivariance(self, atol: float = 1e-6, rtol: float = 1e-5) -> List[Tuple[Any, float]]:
# this kind of pooling is not really equivariant so we can not test equivariance
pass
def export(self):
r"""
Export this module to a normal PyTorch :class:`torch.nn.MaxPool2d` module and set to "eval" mode.
"""
self.eval()
if self.d == 2:
return torch.nn.MaxPool2d(self.kernel_size, self.stride, self.padding, self.dilation).eval()
elif self.d == 3:
return torch.nn.MaxPool3d(self.kernel_size, self.stride, self.padding, self.dilation).eval()
else:
raise NotImplementedError
class _PointwiseMaxPoolAntialiasedND(_PointwiseMaxPoolND):
def __init__(self,
in_type: FieldType,
d: int,
kernel_size: Union[int, Tuple[int, int]],
stride: Union[int, Tuple[int, int]] = None,
padding: Union[int, Tuple[int, int]] = 0,
dilation: Union[int, Tuple[int, int]] = 1,
ceil_mode: bool = False,
sigma: float = 0.6,
):
r"""
Anti-aliased version of channel-wise max-pooling (each channel is treated independently).
The max over a neighborhood is performed pointwise withot downsampling.
Then, convolution with a gaussian blurring filter is performed before downsampling the feature map.
Based on `Making Convolutional Networks Shift-Invariant Again <https://arxiv.org/abs/1904.11486>`_.
Notice that not all representations support this kind of pooling. In general, only representations which support
pointwise non-linearities do.
.. warning ::
Even if the input tensor has a `coords` attribute, the output of this module will not have one.
Args:
in_type (FieldType): the input field type
d (int): dimensionality of the base space (2 for images, 3 for volumes)
kernel_size: the size of the window to take a max over
stride: the stride of the window. Default value is :attr:`kernel_size`
padding: implicit zero padding to be added on both sides
dilation: a parameter that controls the stride of elements in the window
ceil_mode: when ``True``, will use ceil instead of floor to compute the output shape
sigma (float): standard deviation for the Gaussian blur filter
"""
if dilation != 1:
raise NotImplementedError("Dilation larger than 1 is not supported yet")
super(_PointwiseMaxPoolAntialiasedND, self).__init__(in_type, d, kernel_size, stride, padding, dilation, ceil_mode)
assert sigma > 0.
filter_size = 2 * int(round(4 * sigma)) + 1
# Build the Gaussian smoothing filter
grid = torch.stack(torch.meshgrid(*[torch.arange(filter_size)] * self.d, indexing='ij'), dim=-1)
mean = (filter_size - 1) / 2.
variance = sigma ** 2.
# setting the dtype is needed, otherwise it becomes an integer tensor
r = -torch.sum((grid - mean) ** 2., dim=-1, dtype=torch.get_default_dtype())
# Build the gaussian kernel
_filter = torch.exp(r / (2 * variance))
# Normalize
_filter /= torch.sum(_filter)
# The filter needs to be reshaped to be used in 2d depthwise convolution
_filter = _filter.view(1, 1, *[filter_size]*self.d).repeat((in_type.size, 1, *[1]*self.d))
self.register_buffer('filter', _filter)
self._pad = tuple(p + int((filter_size - 1) // 2) for p in self.padding)
def forward(self, input: GeometricTensor) -> GeometricTensor:
r"""
Args:
input (GeometricTensor): the input feature map
Returns:
the resulting feature map
"""
assert input.type == self.in_type
if self.d == 2:
# evaluate the max operation densely (stride = 1)
output = F.max_pool2d(input.tensor,
self.kernel_size,
1,
self.padding,
self.dilation,
self.ceil_mode)
output = F.conv2d(output, self.filter, stride=self.stride, padding=self._pad, groups=output.shape[1])
elif self.d == 3:
# evaluate the max operation densely (stride = 1)
output = F.max_pool3d(input.tensor,
self.kernel_size,
1,
self.padding,
self.dilation,
self.ceil_mode)
output = F.conv3d(output, self.filter, stride=self.stride, padding=self._pad, groups=output.shape[1])
else:
raise NotImplementedError
# wrap the result in a GeometricTensor
return GeometricTensor(output, self.out_type, coords=None)
def export(self):
raise NotImplementedError
[docs]class PointwiseMaxPool2D(_PointwiseMaxPoolND):
def __init__(self,
in_type: FieldType,
kernel_size: Union[int, Tuple[int, int]],
stride: Union[int, Tuple[int, int]] = None,
padding: Union[int, Tuple[int, int]] = 0,
dilation: Union[int, Tuple[int, int]] = 1,
ceil_mode: bool = False
):
r"""
Channel-wise max-pooling: each channel is treated independently.
This module works exactly as :class:`torch.nn.MaxPool2D`, wrapping it in the
:class:`~escnn.nn.EquivariantModule` interface.
Notice that not all representations support this kind of pooling. In general, only representations which support
pointwise non-linearities do.
.. warning ::
Even if the input tensor has a `coords` attribute, the output of this module will not have one.
Args:
in_type (FieldType): the input field type
kernel_size: the size of the window to take a max over
stride: the stride of the window. Default value is :attr:`kernel_size`
padding: implicit zero padding to be added on both sides
dilation: a parameter that controls the stride of elements in the window
ceil_mode: when True, will use ceil instead of floor to compute the output shape
"""
assert isinstance(in_type.gspace, GSpace)
assert in_type.gspace.dimensionality == 2
super(PointwiseMaxPool2D, self).__init__(
in_type, 2, kernel_size, stride, padding, dilation, ceil_mode
)
[docs]class PointwiseMaxPool3D(_PointwiseMaxPoolND):
def __init__(self,
in_type: FieldType,
kernel_size: Union[int, Tuple[int, int]],
stride: Union[int, Tuple[int, int]] = None,
padding: Union[int, Tuple[int, int]] = 0,
dilation: Union[int, Tuple[int, int]] = 1,
ceil_mode: bool = False
):
r"""
Channel-wise max-pooling: each channel is treated independently.
This module works exactly as :class:`torch.nn.MaxPool3D`, wrapping it in the
:class:`~escnn.nn.EquivariantModule` interface.
Notice that not all representations support this kind of pooling. In general, only representations which support
pointwise non-linearities do.
.. warning ::
Even if the input tensor has a `coords` attribute, the output of this module will not have one.
Args:
in_type (FieldType): the input field type
kernel_size: the size of the window to take a max over
stride: the stride of the window. Default value is :attr:`kernel_size`
padding: implicit zero padding to be added on both sides
dilation: a parameter that controls the stride of elements in the window
ceil_mode: when True, will use ceil instead of floor to compute the output shape
"""
assert isinstance(in_type.gspace, GSpace)
assert in_type.gspace.dimensionality == 3
super(PointwiseMaxPool3D, self).__init__(
in_type, 3, kernel_size, stride, padding, dilation, ceil_mode
)
[docs]class PointwiseMaxPoolAntialiased2D(_PointwiseMaxPoolAntialiasedND):
def __init__(self,
in_type: FieldType,
kernel_size: Union[int, Tuple[int, int]],
stride: Union[int, Tuple[int, int]] = None,
padding: Union[int, Tuple[int, int]] = 0,
dilation: Union[int, Tuple[int, int]] = 1,
ceil_mode: bool = False,
sigma: float = 0.6,
):
r"""
Anti-aliased version of channel-wise max-pooling (each channel is treated independently).
The max over a neighborhood is performed pointwise withot downsampling.
Then, convolution with a gaussian blurring filter is performed before downsampling the feature map.
Based on `Making Convolutional Networks Shift-Invariant Again <https://arxiv.org/abs/1904.11486>`_.
Notice that not all representations support this kind of pooling. In general, only representations which support
pointwise non-linearities do.
.. warning ::
Even if the input tensor has a `coords` attribute, the output of this module will not have one.
Args:
in_type (FieldType): the input field type
kernel_size: the size of the window to take a max over
stride: the stride of the window. Default value is :attr:`kernel_size`
padding: implicit zero padding to be added on both sides
dilation: a parameter that controls the stride of elements in the window
ceil_mode: when ``True``, will use ceil instead of floor to compute the output shape
sigma (float): standard deviation for the Gaussian blur filter
"""
super(PointwiseMaxPoolAntialiased2D, self).__init__(
in_type, 2, kernel_size, stride, padding, dilation, ceil_mode, sigma
)
[docs]class PointwiseMaxPoolAntialiased3D(_PointwiseMaxPoolAntialiasedND):
def __init__(self,
in_type: FieldType,
kernel_size: Union[int, Tuple[int, int]],
stride: Union[int, Tuple[int, int]] = None,
padding: Union[int, Tuple[int, int]] = 0,
dilation: Union[int, Tuple[int, int]] = 1,
ceil_mode: bool = False,
sigma: float = 0.6,
):
r"""
Anti-aliased version of channel-wise max-pooling (each channel is treated independently).
The max over a neighborhood is performed pointwise withot downsampling.
Then, convolution with a gaussian blurring filter is performed before downsampling the feature map.
Based on `Making Convolutional Networks Shift-Invariant Again <https://arxiv.org/abs/1904.11486>`_.
Notice that not all representations support this kind of pooling. In general, only representations which support
pointwise non-linearities do.
.. warning ::
Even if the input tensor has a `coords` attribute, the output of this module will not have one.
Args:
in_type (FieldType): the input field type
kernel_size: the size of the window to take a max over
stride: the stride of the window. Default value is :attr:`kernel_size`
padding: implicit zero padding to be added on both sides
dilation: a parameter that controls the stride of elements in the window
ceil_mode: when ``True``, will use ceil instead of floor to compute the output shape
sigma (float): standard deviation for the Gaussian blur filter
"""
super(PointwiseMaxPoolAntialiased3D, self).__init__(
in_type, 3, kernel_size, stride, padding, dilation, ceil_mode, sigma
)
# for backward compatibility
PointwiseMaxPool = PointwiseMaxPool2D
PointwiseMaxPoolAntialiased = PointwiseMaxPoolAntialiased2D